treedist
computes different tree distance methods and RF.dist
the Robinson-Foulds or symmetric distance. The Robinson-Foulds distance only
depends on the topology of the trees. If edge weights should be considered
wRF.dist
calculates the weighted RF distance (Robinson & Foulds
1981). and KF.dist
calculates the branch score distance (Kuhner &
Felsenstein 1994). path.dist
computes the path difference metric as
described in Steel and Penny 1993).
sprdist
computes the approximate SPR distance (Oliveira Martins et
al. 2008, de Oliveira Martins 2016).
treedist(tree1, tree2, check.labels = TRUE)sprdist(tree1, tree2)
SPR.dist(tree1, tree2 = NULL)
RF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
rooted = FALSE)
wRF.dist(tree1, tree2 = NULL, normalize = FALSE, check.labels = TRUE,
rooted = FALSE)
KF.dist(tree1, tree2 = NULL, check.labels = TRUE, rooted = FALSE)
path.dist(tree1, tree2 = NULL, check.labels = TRUE, use.weight = FALSE)
treedist
returns a vector containing the following tree
distance methods
symmetric.difference or Robinson-Foulds distance
branch.score.difference
path.difference
weighted.path.difference
A phylogenetic tree (class phylo
) or vector of trees (an
object of class multiPhylo
). See details
A phylogenetic tree.
compares labels of the trees.
compute normalized RF-distance, see details.
take bipartitions for rooted trees into account, default is unrooting the trees.
use edge.length argument or just count number of edges on the path (default)
Klaus P. Schliep klaus.schliep@gmail.com, Leonardo de Oliveira Martins
The Robinson-Foulds distance between two trees \(T_1\) and \(T_2\) with \(n\) tips is defined as (following the notation Steel and Penny 1993): $$d(T_1, T_2) = i(T_1) + i(T_2) - 2v_s(T_1, T_2)$$ where \(i(T_1)\) denotes the number of internal edges and \(v_s(T_1, T_2)\) denotes the number of internal splits shared by the two trees. The normalized Robinson-Foulds distance is derived by dividing \(d(T_1, T_2)\) by the maximal possible distance \(i(T_1) + i(T_2)\). If both trees are unrooted and binary this value is \(2n-6\).
Functions like RF.dist
returns the Robinson-Foulds distance (Robinson
and Foulds 1981) between either 2 trees or computes a matrix of all pairwise
distances if a multiPhylo
object is given.
For large number of trees the distance functions can use a lot of memory!
de Oliveira Martins L., Leal E., Kishino H. (2008) Phylogenetic Detection of Recombination with a Bayesian Prior on the Distance between Trees. PLoS ONE 3(7). e2651. doi: 10.1371/journal.pone.0002651
de Oliveira Martins L., Mallo D., Posada D. (2016) A Bayesian Supertree Model for Genome-Wide Species Tree Reconstruction. Syst. Biol. 65(3): 397-416, doi:10.1093/sysbio/syu082
Steel M. A. and Penny P. (1993) Distributions of tree comparison metrics - some new results, Syst. Biol., 42(2), 126--141
Kuhner, M. K. and Felsenstein, J. (1994) A simulation comparison of phylogeny algorithms under equal and unequal evolutionary rates, Molecular Biology and Evolution, 11(3), 459--468
D.F. Robinson and L.R. Foulds (1981) Comparison of phylogenetic trees, Mathematical Biosciences, 53(1), 131--147
D.F. Robinson and L.R. Foulds (1979) Comparison of weighted labelled trees. In Horadam, A. F. and Wallis, W. D. (Eds.), Combinatorial Mathematics VI: Proceedings of the Sixth Australian Conference on Combinatorial Mathematics, Armidale, Australia, 119--126
dist.topo
, nni
,
superTree
, mast
tree1 <- rtree(100, rooted=FALSE)
tree2 <- rSPR(tree1, 3)
RF.dist(tree1, tree2)
treedist(tree1, tree2)
sprdist(tree1, tree2)
trees <- rSPR(tree1, 1:5)
SPR.dist(tree1, trees)
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